Predictive Analytics

Presentation given at USI 2014, Paris. Contrasts Big Data vs Analytics and explore how the two interact.

Video of the talk in French: https://www.youtube.com/watch?v=R8QLyBXlEYg

Transcript

1. Predictive Analytics Olivier Grisel — @ogrisel — June 16-17 2014
2. About me
3. Big Data as a buzzword
4. Triumph of the Nerds: Nate Silver Wins in 50 States http://mashable.com/2012/11/07/nate-silver-wins/
5. Triumph of the Nerds: Nate Silver Wins in 50 States http://mashable.com/2012/11/07/nate-silver-wins/
6. Nate Silver’s election model, Big Data? $ git clone gh:jseabold/538model! ! $ du -h 538model/data! 188K 538model/data
7. 15% of the capacity of a 3’5 floppy disk
8. Regionator 3000 http://labs.data-publica.com/regionator3000/
9. http://transports.blog.lemonde.fr/2014/06/05/regionator-la- carte-de-france-dessinee-par-les-trajets-quotidiens/
10. http://transports.blog.lemonde.fr/2014/06/05/regionator-la- carte-de-france-dessinee-par-les-trajets-quotidiens/
11. http://www.insee.fr/fr/themes/detail.asp? reg_id=99&ref_id=mobilite-professionnelle-10
12. http://www.insee.fr/fr/themes/detail.asp? reg_id=99&ref_id=mobilite-professionnelle-10
13. 120% of the capacity of a 3’5 floppy disk
14. Big Data ≠ Predictive Analytics
15. Goals of this talk • What Big Data actually is or isn’t • Introduce predictive analytics concepts & tools • Study the impact of data size on analytics
16. How big is Big Data?
17. – Wikipedia “Big data is a blanket term for any collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.”
18. Not Big Data • Data that fits on a spreadsheet • Data that can be analyzed in RAM (< 100 GB) • Data operations that can be performed quickly by a traditional database, e.g. single node PostgreSQL server
19. Reading the full content of a 1TB HDD at 100MB/s: ! 2 hours 45 minutes
20. Canonical Big Data problem: indexing the Web • Inverted index on tera bytes of text data • Process each HTML page as a URL + bag of words • For each word, aggregate the list of page URLs • 2 billion HTML pages: 100TB >10 days just to read sequentially
23. Non-traditional architectures • Hadoop: HDFS / MapReduce, Pig, Hive • Sharded, replicated NoSQL: BigTable, DynamoDB, Cassandra, HBase, ElasticSearch • Distributed event stream processing Kafka, Storm • Next gen cluster processing / distributed analytical DB YARN / Tez, Spark, Impala, PrestoDB, Redshift…
24. How heavy is a copy of the web?
25. 1TB ≈ 1Kg ! when stored on a hadoop node
26. All HTML pages ≈ 100 TB ≈ 100 Kg
28. All the web ≈ 10 PB ≈ 10 tonnes
29. Other Big Data examples • GSM location event log from telco • Transaction log of a big retail network • Raw traffic data on a large website or app • Intra-day tick data from a stock exchange
30. Not Big Data • Polls data (~10K data points) • Census data (~10M data points) • Real estate transactions data (~10M data points) • Open / High / Low / Close (OHLC) stock prices
 (~10K data points) • Any dataset publicly available for download
31. What is Predictive Analytics? and Machine Learning
32. • Make predictions of outcome on new data • Alternative to hard-coded rules written by experts • Extract the structure of historical data • Statistical tools to summarize the training data into a executable predictive model
33. Type # rooms Surface m2 Floor Public Transports Apartment 3 65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes
34. Type # rooms Surface m2 Floor Public Transports Apartment 3 65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes features samples
35. Type # rooms Surface m2 Floor Public Transports Apartment 3 65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes features samples Sold 300k 1.5M 2.2M target
36. Type # rooms Surface m2 Floor Public Transports Apartment 3 65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes features samples Sold 300k 1.5M 2.2M target Apartment 2 35 3 Yes
37. Type # rooms Surface m2 Floor Public Transports Apartment 3 65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes features samples Sold 300k 1.5M 2.2M target Apartment 2 35 3 Yes ?
38. Applications in Business • Forecast sales, customer churn, traffic, prices • Predict CTR and optimal bid price for online ads • Build computer vision systems for robots in the industry and agriculture • Detect network anomalies, fraud and spams • Recommend products, movies, music
39. Applications in Science • Decode the activity of the brain recorded via fMRI / EEG / MEG • Decode gene expression data to model regulatory networks • Predict the distance of each star in the sky • Identify the Higgs boson in proton-proton collisions
40. Training! text docs! images! sounds! transactions Predictive Modeling Data Flow
41. Training! text docs! images! sounds! transactions Labels Predictive Modeling Data Flow
42. Training! text docs! images! sounds! transactions Labels Machine! Learning! Algorithm Predictive Modeling Data Flow Feature vectors
43. Training! text docs! images! sounds! transactions Labels Machine! Learning! Algorithm Model Predictive Modeling Data Flow Feature vectors
44. New! text doc! image! sound! transaction Model Expected! Label Predictive Modeling Data Flow Feature vector Training! text docs! images! sounds! transactions Labels Machine! Learning! Algorithm Feature vectors
45. Tools for predictive analytics
46. SPSS MATLAB
47. SPSS MATLAB
48. New! text doc! image! sound! transaction Model Expected! Label Small data Training! text docs! images! sounds! transactions Labels Machine! Learning! Algorithm Feature vectors Feature vector
49. New! text doc! image! sound! transaction Model Expected! Label Small / Medium data Training! text docs! images! sounds! transactions Labels Machine! Learning! Algorithm Feature vectors Feature vector
50. New! text doc! image! sound! transaction Model Expected! Label Small / Medium data with Training! text docs! images! sounds! transactions Labels Machine! Learning! Algorithm Feature vectors Feature vector
51. New! text doc! image! sound! transaction Model Expected! Label Small / Medium data with Training! text docs! images! sounds! transactions Labels Machine! Learning! Algorithm Feature vectors Feature vector
54. Predictive Analytics on Big Data
55. Model Expected! Label Big data with Machine! Learning! Algorithm New! text doc! image! sound! transaction Training! text docs! images! sounds! transactions Labels Feature vectors Feature vector
56. Model Expected! Label Big data with Machine! Learning! Algorithm New! text doc! image! sound! transaction Training! text docs! images! sounds! transactions Labels Feature vectors Feature vector
57. Model Expected! Label Big data with Machine! Learning! Algorithm New! text doc! image! sound! transaction Training! text docs! images! sounds! transactions Labels Feature vectors Feature vector
59. BIG DATA
60. BIG DATA small(er) data
61. BIG DATA small(er) data
62. From Big to Small • Feature extraction often shrinks data • Filter / Join / Group By / Count • Machine Learning performed on a small aggregate • Sampling for fast in-memory iterative modeling
63. Back to the Regionator What if we did not have census data on daily mobility?
64. Back to the Regionator • Use raw daily telco logs • Group By (phone, day) to extract daily trips • Join By GPS coordinates to “departement” names • Filter out small trips • Group By (home, work) “departements” • Count
65. Data size and modeling quality
66. – Peter Norvig, Research Director, Google “We don’t have better algorithms. We just have more data.”
70. More data beats better models?
71. http://technocalifornia.blogspot.fr/2012/07/more-data-or-better- models.html
72. Let’s train a parametric model to read handwritten digits from gray level pixels.
74. model stops improving
76. Bias vs Variance
77. high bias
78. high bias high variance
79. high bias high variance low variance
80. Variance solution #1: collect more samples
81. Let’s train a non-parametric model to read handwritten digits from gray level pixels.
83. high variance almost no bias ! variance decreasing with #samples
84. Bias solution #1: non-parametric models
85. Type # rooms Surface (m2) Floor Public Transp. Apart. 3 65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes features samples
86. Type # rooms Surface (m2) Floor Public Transp. School (km) Flood plain Apart. 3 65 2 Yes 1.0 No House 5 110 NA No 25.0 Yes Duplex 4 95 4 Yes 0.5 No features samples
87. Bias solution #2: richer features
88. Data has 2 dimensions: ! # samples and # features
89. Key takeaway points:
90. • Big Data ≠ Predictive Analytics • Predictive models are often built from small aggregate data (with sampling) << raw data • Modeling requires interactive / fast iterations • More data generally helps build better models but not always: noise or inadequate repr. • 2 dimensions: # samples & # features
91. Thank you! Questions? ! @Inria @ogrisel http://scikit-learn.org
92. Bonus track
93. • Parametric e.g. linear model (traditional stats) vs Non-parametric e.g. Random Forests, Neural Networks (Machine Learning) • Understand a model with 10% accuracy vs blindly trust a model with 90% accuracy • Simple models e.g. F = m a, F = - G (m1 + m2) / r^2 will not become false(r) because of big data • New problems can be tackled: computer vision, speech recognition, natural language understanding
94. • the (experimental) scientific method introduced by Karl Popper is based on the falsifiability of formulated hypotheses • theory is correct as long as past predictions hold in new experiments • machine learning train-validation-test splits and cross-validation is similar in spirit • ml model is just a complex theory: correct as long as its predictions still hold